Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network

• URL: http://arxiv.org/abs/2307.08268v2
• Date: Sat, 21 Oct 2023 14:29:06 GMT
• Title: Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network
• Authors: Ke Yan, Xiaoli Yin, Yingda Xia, Fakai Wang, Shu Wang, Yuan Gao, Jiawen Yao, Chunli Li, Xiaoyu Bai, Jingren Zhou, Ling Zhang, Le Lu, Yu Shi
• Abstract summary: Pixel-Lesion-pAtient Network (PLAN) is proposed to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation.
• Score: 37.931408083443074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Liver tumor segmentation and classification are important tasks in computer aided diagnosis. We aim to address three problems: liver tumor screening and preliminary diagnosis in non-contrast computed tomography (CT), and differential diagnosis in dynamic contrast-enhanced CT. A novel framework named Pixel-Lesion-pAtient Network (PLAN) is proposed. It uses a mask transformer to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. It also has an image-wise classifier to effectively aggregate global information and predict patient-level diagnosis. A large-scale multi-phase dataset is collected containing 939 tumor patients and 810 normal subjects. 4010 tumor instances of eight types are extensively annotated. On the non-contrast tumor screening task, PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation. We also conduct a reader study on a holdout set of 250 cases. PLAN is on par with a senior human radiologist, showing the clinical significance of our results.

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